YOLOv5モデルをHugging Face Hub & Spacesにデプロイする手順

yolov5-kunshujo/
├── app.py               # モデルをローカルから起動時にロード
├── best.pt              # 699MB のモデルをリポジトリに直接格納
├── requirements.txt     # 不要パッケージ（gdown, matplotlib, seaborn）含む
└── ultralytics/
    └── yolov5/          # YOLOv5 ソースコードをローカルにコピー
        ├── hubconf.py
        ├── models/
        └── utils/

yolov5-kunshujo/
├── app.py               # HF Hubからモデルをダウンロード・遅延ロード
└── requirements.txt     # 必要なパッケージのみに整理

pip install huggingface_hub

# フルパスで実行
/Users/nakamura/Library/Python/3.9/bin/hf login

# または永続的にPATHを追加
echo 'export PATH="$HOME/Library/Python/3.9/bin:$PATH"' >> ~/.zshrc
source ~/.zshrc

hf repo create yolov5-kunshujo --repo-type model

hf upload nakamura196/yolov5-kunshujo ./best.pt best.pt

---
license: apache-2.0
tags:
  - object-detection
  - yolov5
  - pytorch
pipeline_tag: object-detection
---

# YOLOv5 Kunshujo

...

hf upload nakamura196/yolov5-kunshujo ./README.md README.md

REPO_ID = "nakamura196/yolov5-kunshujo"

from huggingface_hub import hf_hub_download

_model = None

def get_model():
    global _model
    if _model is not None:
        return _model
    model_path = hf_hub_download(repo_id=REPO_ID, filename=MODEL_FILENAME)
    # ... モデルロード
    return _model

torch
torchvision
Pillow
opencv-python-headless
ipython
scipy
pandas
matplotlib
seaborn
psutil

# Patch gradio_client bug: additionalProperties: True (bool) causes TypeError/APIInfoParseError
import gradio_client.utils as _gc_utils
_orig_j2p = _gc_utils._json_schema_to_python_type
def _patched_j2p(schema, defs):
    if not isinstance(schema, dict):
        return "any"
    return _orig_j2p(schema, defs)
_gc_utils._json_schema_to_python_type = _patched_j2p

import gradio as gr
import torch
from PIL import Image, ImageDraw
from huggingface_hub import hf_hub_download

REPO_ID = "nakamura196/yolov5-kunshujo"
MODEL_FILENAME = "best.pt"

_model = None


def get_model():
    global _model
    if _model is not None:
        return _model

    model_path = hf_hub_download(repo_id=REPO_ID, filename=MODEL_FILENAME)

    # PyTorch 2.6+ weights_only=True breaking change workaround
    _original_load = torch.load
    torch.load = lambda *a, **kw: _original_load(*a, **{**kw, "weights_only": False})
    try:
        _model = torch.hub.load("ultralytics/yolov5:v7.0", "custom", path=model_path, trust_repo=True)
    finally:
        torch.load = _original_load

    return _model


def yolo(im, size=1024):
    model = get_model()
    g = (size / max(im.size))
    im = im.resize(tuple(int(x * g) for x in im.size), Image.BICUBIC)

    results = model(im)
    res = results.pandas().xyxy[0].to_dict(orient="records")

    im_draw = im.copy()
    draw = ImageDraw.Draw(im_draw)

    detected_images = []
    for item in res:
        xmin, ymin, xmax, ymax = item['xmin'], item['ymin'], item['xmax'], item['ymax']
        draw.rectangle([(xmin, ymin), (xmax, ymax)], outline="red", width=2)
        detected_images.append(im.crop((xmin, ymin, xmax, ymax)))

    return [res, im_draw, detected_images]


inputs = [gr.Image(type='pil', label="Original Image")]
outputs = [
    gr.JSON(label="JSON Output"),
    gr.Image(type="pil", label="Output Image"),
    gr.Gallery(label="Detected Objects"),
]

demo = gr.Interface(
    yolo, inputs, outputs,
    title="YOLOv5 Kunshujo",
    description="YOLOv5 Kunshujo Gradio demo for object detection.",
    examples=[['2586696_R0000008.jpg'], ['2586696_R0000009.jpg']],
    flagging_mode="never",
)

demo.launch(show_error=True, server_name="0.0.0.0", server_port=7860, ssr_mode=False)

TypeError: argument of type 'bool' is not iterable
# または
gradio_client.utils.APIInfoParseError: Cannot parse schema True

import gradio_client.utils as _gc_utils
_orig_j2p = _gc_utils._json_schema_to_python_type
def _patched_j2p(schema, defs):
    if not isinstance(schema, dict):
        return "any"
    return _orig_j2p(schema, defs)
_gc_utils._json_schema_to_python_type = _patched_j2p

Weights only load failed. WeightsUnpickler error: Unsupported global:
GLOBAL numpy.core.multiarray._reconstruct was not an allowed global by default.

_original_load = torch.load
torch.load = lambda *a, **kw: _original_load(*a, **{**kw, "weights_only": False})
try:
    _model = torch.hub.load("ultralytics/yolov5:v7.0", "custom", path=model_path, trust_repo=True)
finally:
    torch.load = _original_load

argument 1 must be 2-item sequence, not generator

# Before
im = im.resize((int(x * g) for x in im.size), Image.BICUBIC)

# After
im = im.resize(tuple(int(x * g) for x in im.size), Image.BICUBIC)

ModuleNotFoundError: No module named 'pyaudioop'

sdk_version: 5.9.1

# Before (gradio 4.x)
demo = gr.Interface(..., allow_flagging="never")

# After (gradio 5.x)
demo = gr.Interface(..., flagging_mode="never")

_model = torch.hub.load("ultralytics/yolov5:v7.0", "custom", path=model_path, trust_repo=True)

_model = torch.hub.load("ultralytics/yolov5:v7.0", "custom", path=model_path, trust_repo=True)

ModuleNotFoundError: No module named 'cv2'
ModuleNotFoundError: No module named 'IPython'
ModuleNotFoundError: No module named 'matplotlib'
ModuleNotFoundError: No module named 'seaborn'
ModuleNotFoundError: No module named 'psutil'

demo.launch(
    show_error=True,
    server_name="0.0.0.0",   # ← 127.0.0.1 から変更
    server_port=7860,
    ssr_mode=False,           # ← SSR を無効化
)